Face recognition using holistic Fourier invariant features

Lai, Jian; Yuen, Pong C.; Feng, Guocan

doi:10.1016/s0031-3203(99)00200-9

Cited by 150 publications

(63 citation statements)

References 34 publications

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“…In most cases, the high-frequency subbands are used for recognition because they are insensitive to illumination variations. Representative works include using wavelet transform in Waveletface [40], extraction of Gabor feature [14], using discrete cosine transform (DCT) [41], using discrete Fourier transform (DFT) in the Spectroface [42], and the dual-tree complex wavelet transform (DT-CWT)-based method [5]. In the image space, the set of images under all possible lighting directions for an object with a fixed pose form a convex cone, which is called the illumination cone [23].…”

Section: ) Category I: Extracting Illumination Insensitive Featuresmentioning

confidence: 99%

Normalization of Face Illumination Based on Large-and Small-Scale Features

Xie

Zheng

Lai

et al. 2011

IEEE Trans. on Image Process.

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132

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Abstract-A face image can be represented by a combination of large-and small-scale features. It is well-known that the variations of illumination mainly affect the large-scale features (low-frequency components), and not so much the small-scale features. Therefore, in relevant existing methods only the small-scale features are extracted as illumination-invariant features for face recognition, while the large-scale intrinsic features are always ignored. In this paper, we argue that both large-and small-scale features of a face image are important for face restoration and recognition. Moreover, we suggest that illumination normalization should be performed mainly on the large-scale features of a face image rather than on the original face image. A novel method of normalizing both the Small-and Large-scale (S&L) features of a face image is proposed. In this method, a single face image is first decomposed into large-and small-scale features. After that, illumination normalization is mainly performed on the large-scale features, and only a minor correction is made on the small-scale features. Finally, a normalized face image is generated by combining the processed large-and small-scale features. In addition, an optional visual compensation step is suggested for improving the visual quality of the normalized image. Experiments on CMU-PIE, Extended Yale B, and FRGC 2.0 face databases show that by using the proposed method significantly better recognition performance and visual results can be obtained as compared to related state-of-the-art methods.

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Section: ) Category I: Extracting Illumination Insensitive Featuresmentioning

confidence: 99%

Normalization of Face Illumination Based on Large-and Small-Scale Features

Xie

Zheng

Lai

et al. 2011

IEEE Trans. on Image Process.

Self Cite

132

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“…A number of practical advantages of the holistic approach were described in literature [14]. Firstly, locating facial features is not required since the face is handled as a global feature.…”

Section: Resultsmentioning

confidence: 99%

Generating Facial Composites from Principal Components

Zahradníková¹,

Duchovičová²,

Schreiber³

2016

MATEC Web of Conferences

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Abstract. Penetrating computers and process automation into all the fields of human activity meant a breakthrough in the research of forensic methods applied on facial composite production. Although the issues stemming from unsatisfying graphical presentation of composites have been resolved by implementing computational technique into creating facial composites, software developed and used by the police in the most countries of the world still has not achieved satisfactory results. The research presented in this paper aims at developing an alternative technique respecting human ability of recognizing faces and eliminating the limitations of the previous systems as identified by psychologists within several years. The paper is concerned with a novel method of generating facial pictures. We presented the initial research on holistic and hybrid model and demonstrated the ability of generating novel facial images based on the proposed solution.

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“…In [8], [9], authors proposed a novel technique to detect the pests at some initial stages. This idea was an excellent approach towards the detection of pests, but with some limitations.…”

Section: Related Workmentioning

confidence: 99%

K-means Based Automatic Pests Detection and Classification for Pesticides Spraying

Javed¹,

Noor²,

Khan³

et al. 2017

ijacsa

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Abstract-Agriculture is the backbone to the living being that plays a vital role to country's economy. Agriculture production is inversely affected by pest infestation and plant diseases. Plants vitality is directly affected by the pests as poor or abnormal. Automatic pest detection and classification is an essential research phenomenon, as early detection and classification of pests as they appear on the plants may lead to minimizing the loss of production. This study puts forth a comprehensive model that would facilitate the detection and classification of the pests by using Artificial Neural Network (ANN). In this approach, the image has been segmented from the fields by using enhanced K-Mean segmentation technique that identifies the pests or any object from the image. Subsequently, features will be extracted by using Discrete Cosine Transform (DCT) and classified using ANN to classify pests. The proposed approach is verified for five pests that exhibited 94% effectiveness while classifying the pests.

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Face recognition using holistic Fourier invariant features

Cited by 150 publications

References 34 publications

Normalization of Face Illumination Based on Large-and Small-Scale Features

Normalization of Face Illumination Based on Large-and Small-Scale Features

Generating Facial Composites from Principal Components

K-means Based Automatic Pests Detection and Classification for Pesticides Spraying

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